 
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
 
from datetime import datetime
import math
import time
 
import numpy as np
import tensorflow as tf
 
import cifar10
 
parser = cifar10.parser
 
parser.add_argument('--eval_dir', type=str, default='./cifar10_eval',
                    help='Directory where to write event logs.')
 
parser.add_argument('--eval_data', type=str, default='test',
                    help='Either `test` or `train_eval`.')
 
parser.add_argument('--checkpoint_dir', type=str, default='./cifar10_train',
                    help='Directory where to read model checkpoints.')
 
parser.add_argument('--eval_interval_secs', type=int, default=60*5,
                    help='How often to run the eval.')
 
parser.add_argument('--num_examples', type=int, default=10000,
                    help='Number of examples to run.')
 
parser.add_argument('--run_once', type=bool, default=False,
                    help='Whether to run eval only once.')
 
 
#cifar10_train.py 会周期性的在检查点文件中保存模型中的所有参数，但是不会对模型进行评估。
# cifar10_eval.py 会使用该检查点文件在另一部分数据集上测试预测性能。
# 利用 inference() 函数重构模型，并使用了在评估数据集所有10,000张 CIFAR-10 图片进行测试。
# 最终计算出的精度为 1 : N，N = 预测值中置信度最高的一项与图片真实 label 匹配的频次。
# 为了监控模型在训练过程中的改进情况，评估用的脚本文件会周期性的在最新的检查点文件上运行，
# 这些检查点文件是由上述的 cifar10_train.py 产生
def eval_once(saver, summary_writer, top_k_op, summary_op):
  """Run Eval once.
  Args:
    saver: Saver.
    summary_writer: Summary writer.
    top_k_op: Top K op.
    summary_op: Summary op.
  """
  with tf.Session() as sess:
    ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
    if ckpt and ckpt.model_checkpoint_path:
      # Restores from checkpoint
      saver.restore(sess, ckpt.model_checkpoint_path)
      # Assuming model_checkpoint_path looks something like:
      #   /my-favorite-path/cifar10_train/model.ckpt-0,
      # extract global_step from it.
      global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
    else:
      print('No checkpoint file found')
      return
 
    # Start the queue runners.
    coord = tf.train.Coordinator()
    try:
      threads = []
      for qr in tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS):
        threads.extend(qr.create_threads(sess, coord=coord, daemon=True,
                                         start=True))
 
      num_iter = int(math.ceil(FLAGS.num_examples / FLAGS.batch_size))
      true_count = 0  # Counts the number of correct predictions.
      total_sample_count = num_iter * FLAGS.batch_size
      step = 0
      while step < num_iter and not coord.should_stop():
        predictions = sess.run([top_k_op])
        true_count += np.sum(predictions)
        step += 1
 
      # Compute precision @ 1.
      precision = true_count / total_sample_count
      print('%s: precision @ 1 = %.3f' % (datetime.now(), precision))
 
      summary = tf.Summary()
      summary.ParseFromString(sess.run(summary_op))
      summary.value.add(tag='Precision @ 1', simple_value=precision)
      summary_writer.add_summary(summary, global_step)
    except Exception as e:  # pylint: disable=broad-except
      coord.request_stop(e)
 
    coord.request_stop()
    coord.join(threads, stop_grace_period_secs=10)
 
 
def evaluate():
  """Eval CIFAR-10 for a number of steps."""
  with tf.Graph().as_default() as g:
    # Get images and labels for CIFAR-10.
    eval_data = FLAGS.eval_data == 'test'
    images, labels = cifar10.inputs(eval_data=eval_data)
 
    # Build a Graph that computes the logits predictions from the
    # inference model.
    logits = cifar10.inference(images)
 
    # Calculate predictions.
    top_k_op = tf.nn.in_top_k(logits, labels, 1)
 
    # Restore the moving average version of the learned variables for eval.
    variable_averages = tf.train.ExponentialMovingAverage(
        cifar10.MOVING_AVERAGE_DECAY)
    variables_to_restore = variable_averages.variables_to_restore()
    saver = tf.train.Saver(variables_to_restore)
 
    # Build the summary operation based on the TF collection of Summaries.
    summary_op = tf.summary.merge_all()
 
    summary_writer = tf.summary.FileWriter(FLAGS.eval_dir, g)
 
    while True:
      eval_once(saver, summary_writer, top_k_op, summary_op)
      if FLAGS.run_once:
        break
      time.sleep(FLAGS.eval_interval_secs)
 
 
def main(argv=None):  # pylint: disable=unused-argument
  cifar10.maybe_download_and_extract()
  if tf.gfile.Exists(FLAGS.eval_dir):
    tf.gfile.DeleteRecursively(FLAGS.eval_dir)
  tf.gfile.MakeDirs(FLAGS.eval_dir)
  evaluate()
 
 
if __name__ == '__main__':
  FLAGS = parser.parse_args()
  tf.app.run()
 
#在训练脚本会为所有学习变量计算其滑动均值(Moving Average)，
# 评估脚本则直接将所有学习到的模型参数替换成对应的滑动均值，这一替代方式可以在评估过程中提升模型的性能。
 
#tensorboard  --logdir=D:\tmp\cifar10_train
